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Smart Structures and Systems
  Volume 29, Number 5, May 2022 , pages 729-746
DOI: https://doi.org/10.12989/sss.2022.29.5.729
 


Nonlinear structural model updating based on the Deep Belief Network
Ye Mo, Zuo-Cai Wang, Genda Chen, Ya-Jie Ding and Bi Ge

 
Abstract
    In this paper, a nonlinear structural model updating methodology based on the Deep Belief Network (DBN) is proposed. Firstly, the instantaneous parameters of the vibration responses are obtained by the discrete analytical mode decomposition (DAMD) method and the Hilbert transform (HT). The instantaneous parameters are regarded as the independent variables, and the nonlinear model parameters are considered as the dependent variables. Then the DBN is utilized for approximating the nonlinear mapping relationship between them. At last, the instantaneous parameters of the measured vibration responses are fed into the well-trained DBN. Owing to the strong learning and generalization abilities of the DBN, the updated nonlinear model parameters can be directly estimated. Two nonlinear shear-type structure models under two types of excitation and various noise levels are adopted as numerical simulations to validate the effectiveness of the proposed approach. The nonlinear properties of the structure model are simulated via the hysteretic parameters of a Bouc-Wen model and a Giuffre-Menegotto-Pinto model, respectively. Besides, the proposed approach is verified by a three-story shear-type frame with a piezoelectric friction damper (PFD). Simulated and experimental results suggest that the nonlinear model updating approach has high computational efficiency and precision.
 
Key Words
    DBN; instantaneous parameters; nonlinear model updating; vibration responses
 
Address
Ye Mo: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui, 23009, China
Zuo-Cai Wang: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui, 23009, China; Anhui Engineering Research Center for Civil Engineering Disaster Prevention and Mitigation, Hefei, Anhui, 230009, China
Genda Chen: Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, 65409, USA
Ya-Jie Ding: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui, 23009, China
Bi Ge: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui, 23009, China
 

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